Technical Efficiency of Chinas Banking Industry - Literature review Example

? Technical Efficiency of China’s Banking Industry: Comparison of owned Banks and Joint-stock Commercial Banks using a Stochastic Frontier Approach Name Course Institution Date Table of Contents 2.0 Literature Review 2 2.1Introduction 2 2.2Overview of China’s Banking Industry 2 2.3Description of Efficiency Theories 5 2.3.1Economic Efficiency 5 2.3.2Technical Efficiency 7 2.3.2.1Principal Agent Theory of Efficiency 7 2.3.2.2Definitions and Measures of Technical Efficiency 8 2.3.2.3Parametric Methods of Measurement 8 2.3.2.3.1Stochastic Frontier Approach (SFA) 9 2.3.2.3.1.1Overview of SFA 9 2.3.2.3.1.2Estimation of Technical Efficiency through SFA 10 2.3.2.4Non Parametric Methods of Measurements 12 2.3.2.4.1Data Envelopment Analysis (DEA) 13 2.3.2.4.2Free Disposal Hull (FDH) 13 2.4Previous empirical studies that used SFA 13 2.5Critique and Application of SFA on Chinese banking system 19 2.6Conclusion 20 Bibliography 21 2.0 Literature Review 2.1 Introduction Contemporary global economies operate amidst many turbulences and dynamisms. Nonetheless, most economies have been able to exhibit resilience and remained stronger. Economic turbulences and dynamisms have affected different industries within various global economies. China’s banking industry has not been spared from such turbulences and dynamisms hence the need to analyze its technical efficiency. The following chapter provides a chronological description and critique of relevant theories in respect to technical efficiency within China’s banking industry. The chronological description and critical review entails empirical papers linked to the concept of the study. Different theories of efficiency with respect to technical efficiency are discussed within this chapter. 2.2 Overview of China’s Banking Industry China has being operating economic and financial system on the basis of social principles until 1978. Amazingly, the People’s Bank of China (PBC) had for a long time been in-charge of issuing currencies as well as being the financial hub of all the economic plans of China. After 1978, China realized the need for serious economic and financial reforms. The objective of such reforms was to increase economic and technical efficiency of financial and economic sectors within the country (Jiang, 2001). Jiang (2001) adds that China aimed at enhancing resource allocation through such reforms. Albeit gradual, serious reforms were carried out in major sectors of the economy, banking being the main recipient (Adams, Berger, & Sickles, 1999). China decided effect the reforms in two main stages; from 1979 to 1992 and from 1993 to the present time. The first stage was characterized by development of two tier banking systems; People’s Bank of China (Central Bank) and four state-owned banks that included Bank of China (BOC), China Construction Bank (CCB), Agricultural Bank of China (ABC), and Commercial Bank of China (CBC). Despite high degrees of functional segmentations, these banks were permitted to accept deposits and offer credit facilities to households and corporate organizations by 1985 (China Daily, 2006). The first stage formed the basis of further reforms, which was characterized with development of small and medium sized commercial banks. The main reason for allowing entrance of commercial banks within Chinese banking system was to enhance competition, which was aimed at providing high quality and differentiated services and products (Jiang, 2001). Examples of small and medium commercial banks created during this period included CITI Industrial Bank, Guangdong Development Banks (GDB), China Merchants Bank (CMB), Hua Xia Bank (HXB), and China Everbright Bank (CEB) (China Daily, 2006). Notably, most of these commercial banks were joint-stock owned unlike the previously mentioned state-owned. The second stage, which was flagged off by State Council in 1993 saw various decisions made within the financial system reforms (Leigh and Podpiera, 2006). In this stage, the main aim was to enhance competition within the banking sector so as to create vistas for provision of high quality services and products (People’s Daily, 2004). The second stage in the reform, which is still underway, has led to the current Chinese banking system composed of (Jiang, 2001): PBC, the central bank; charged with the responsibility of implementing the nation’s monetary policies. China Banking Regulatory Commission, which was formed in 2003; providing supervisory role on commercial banks. Policy banks (3 in number) created and funded through issuing of state bonds and loans by PBC State-owned commercial banks (4 in number) that engage in wholesale and retail banking services. Joint stock banks (11) offering retail and wholesale banking products and services 111 City Commercial Banks under the ownership of local government, households, and local enterprises. They provide commercial baking services that include intermediary, money transfers, and settlements amongst others 3 Rural Commercial Banks 758 Urban Credit Cooperatives operating mainly within urban areas 35,544 Rural Credit Coops that engage in supplying fundamental banking products 204 foreign bank subsidiaries by 2004 The following two tables provide an overview of the various key commercial banks and the market share of different forms of banks within Chinese banking system. Table 1: China's Key Commercial Banks Type Name of Bank Banks State-owned Commercial Banks Industrial and Commercial Bank of China (ICBC) Agricultural Bank of China (ABC) Bank of China (BOC) China Construction Bank (CCB) Joint-stock Commercial Bank of Communication (BOCOM) CITIC Industrial Bank (CITICIB) China Merchants Bank (CMB) Shenzhen Development Bank (SDB) Guangdong Development Bank (GDB) Industrial Bank (IB, known as Fujian Industrial Bank before March 2003) China Everbright Bank (CEB) Hua Xia Bank (HXB) Shanghai Pudong Development Bank (SPDB) China Minsheng Banking Corporation (CMBC) Source: Almanac of China’s Finance and Banking (2003) Table 2: Market Share of Chine banks 1992 to 2002 Source: Flu (2007), and Almanac of China’s Finance and Banking (2003) 2.3 Description of Efficiency Theories In general terms, efficiency refers to the maximum output that a firm can produced from specific amount of inputs. In this perspective, there are two components of efficiency in with respect to productivity; purely technical or physical and allocative or price components (Coelli, et al. 2005. These components when combined together form the broader aspects of economic efficiency (Adams, Berger, & Sickles, 1999). The following discussions provide a brief overview of the two components of efficiency with much emphasis on the technical efficiency as this is the backbone of this research. Therefore, from this perspective it is true that Chinese banking industry has both technical and allocative component of efficiency despite the fact that this research paper focuses on the former component and not the latter efficiency component. 2.3.1 Economic Efficiency Economic efficient is a combination of both technical and allocative efficiency. It is important to give a brief description of allocative efficiency since technical efficiency is largely discussed in this paper (Greene, 2000). Allocative efficiency or price component of efficiency is whereby a firm has the ability of combining inputs and outputs in appropriately optimal proportions whilst taking considerations of the prices of the inputs and outputs (Huang, et al. 2011). The main aim of appropriately applying the right proportion of inputs and outputs is to enhance production of adequate and high quality products (Du and Girma, 2011). The condition necessary and sufficient for allocative efficient is price being equal to marginal costs. Allocative efficiency (Sufian, 2010); …………………………..equation 1 The following figure provides an illustration of combined allocative and technical efficiencies resulting into economic efficiency. Table 3: Economic Efficiency (Allocative and Technical Efficiencies) Source: Flu (2007) In the above diagram, a firm that has an input ratio represented by the isocost line, AA’ in the above diagram, it will be easier to obtain allocative efficiency from the diagram. The firm’s allocative efficiency is represented by PQ of a production unit that operates at P is defined by the ratio indicated in the following formula: …………………………………….equation 2 This follows the concept that the costs of production reduction is presented by RQ. In this case, the firm will be allocatively and technically efficient at point Q’ but will be technically efficient and allocatively inefficient at point Q (Grigorian and Manole, 2002). Therefore, the economic efficiency (EE), which is represented by a combination of allocative and technical efficiencies, will be obtained from the ratio: …………………………………………….equation 3 2.3.2 Technical Efficiency A lot of concentration will be given to technical efficiency since this research aims at identifying technical efficiency of Chinese banking system through comparing state-owned and joint-stock banks (Hu, Su & Chen, 2008). Technical efficiency details will include its definition, measurements through both parametric and non-parametric measurement methods. In addition, two other methods of measurements are discussed (Grigorian and Manole, 2006). Notably, since the concentration is on Stochastic Frontier Approach, much attention is given with the others just being mentioned for deeper understanding. 2.3.2.1 Principal Agent Theory of Efficiency Principal-agent theory or relationships have been used to identify and evaluate the efficiencies of different organizations. In this theory, quality and internal operations of the involved organizations are identified and evaluated (Xu, 2011). The internal analysis involves identification of the relationship between banking industry in this case and their main departments. Therefore, asymmetric information amongst workers and banking industry does not have serious harm on the efficiency of the banks. The general reason for this idea is that when the efficiency of banks is increased then there is likelihood that quality will not seem to reduce hence the concept (Xu, 2011). The main problem of causal factor that can make it difficult to attain required efficiency within Chinese banking firms when principal agent theory is used is when departments of the banks in questions operate incongruently from the main functioning of the banks (Sufian, 2009). 2.3.2.2 Definitions and Measures of Technical Efficiency Technical efficiency is a component of economic efficiency besides allocative efficiency component. According to Huang, et al. (2011), technical efficiency refers to ability of a firm to avoid wastage of inputs during the production process either by producing as much output as the employed technology and input usage or through application of as little input as demanded by technology and output production. In this argument, technical efficiency has both input conserving and output promoting concepts (Inui, et. al. 2008). From this perspective, a firm is technically efficient when the same amount of output is produced with lesser than input that is normally employed in producing the same output. 2.3.2.3 Parametric Methods of Measurement In an attempt to measure technical efficiency (TE), parametric measurement methods specify functional form of the underlying technology whilst outlining the use of econometric approaches for purposes of efficiency estimations (Fu and Heffernan, 2007). Parametric measurement methods employ three main assumptions that determine the form of measurement that provides the best efficiency estimation (Grigorian and Manole, 2002). The three main assumptions include: i. Functional form of the frontier ii. Whether random error is included iii. If the random errors is included then the probability distribution under which efficiency scores are assumed to fall. Notably, parametric measurement methods have advantages and shortcomings, which may determine their applicability in estimating efficiency (Inui, et. al. 2008). The main shortcoming however that prevents wide application of parametric method of measuring technical efficiency is the fact that it has to assume a functional form for the entire production process (Kibaara, 2005). In addition, parametric method of measuring TE is rigid especially with respect to general technologies that are evaluated through multiple outputs and inputs (Hu, Su & Chen, 2008). The following is an example of a parametric method for measuring efficiency. 2.3.2.3.1 Stochastic Frontier Approach (SFA) 2.3.2.3.1.1 Overview of SFA SFA also referred to as the Econometric Frontier Approach (EFA) forms the basis of this research hence a deeper literature review on the same. As a parametric method, SFA specifies functional forms for profits, costs, or production correlation between inputs, outputs, and environmental factors (Adams, Berger, & Sickles, 1999). One of the main reasons as to why economists and statisticians employ the use of SFA significantly is the fact that this parametric method considers random noises around estimated frontiers of production applicable in the technical efficiency estimation (Fu and Heffernan, 2007). For instance, where a single output has been produced from multiple inputs, the SFA predicts the outputs from inputs through application of the functional form of: …………………………….equation 4 Where, i denotes the production unit that is under evaluation, in this case Chinese banking system whereas is the parameter under estimation (Sufian, 2010). From the concept of random error, V­i, the residual is composed whilst the inefficiency component (usually denoted by Ui) is estimated as well (Greene, 2000). There are two scenarios when employing the use of SFA in estimating technical efficiency of a given industry or firm. The first scenario is when the model assumes that V­i = 0 and the second scenario when the model assumes that Ui = 0 (Kwan & Eisenbeis, 1996). The first scenario will reduce the SFA to a deterministic frontier analysis whereas the second scenario reduces the SFA model to central tendency analysis. Application of SFA, also referred to as econometric approach is preferred to many other approaches due to two main reasons; econometric approach is stochastic and parametric (Tortosa-Ausina, et. al. 2008). Being stochastic helps the SFA model to be able to distinguish noise from the effects of inefficiency thereby proving a very accurate estimate of technical efficiency (Kumbhakar and Wang, 2000). On the other hand, by being parametric, SFA can not only test hypotheses but also confounds various effects of misspecification of the functional form, which encompasses both technology and inefficiency. 2.3.2.3.1.2 Estimation of Technical Efficiency through SFA A general form of SFA is provided by the functional form: …………………………equation 5 In this case, is the two-sided noise component that interferes with technical efficiency whereas is the nonnegative technical efficient component usually regarded as the error component. In estimating TE, and are considered to be independently identical, symmetric, and independently distributed. From the above discussion, it therefore follows the error term, which is represented by the following functional form is asymmetrical given the fact that;; the error term is given by: …………………….equation 6 With assumptions that and are distributed independently of, applying Ordinary Least Squares (OLS) on equation 6 will give consistent estimates of all the parameters other than following the concept that ……………..equation 7 Notably, given the fact that OLS does not provide estimates of producer-specific TE, there is need to estimate the producer-specific inefficiency (), which forms the basis or foundation of estimating the overall TE (Greene, 2000). Therefore, in incorporating the producer-specific estimates to obtain the total TE, the following function is applied: ………………………..equation 8 From the above functional form, [], forms the stochastic frontier approach (SFA) also known as the stochastic production frontier. From equation 8, TE can be estimated by making it the subject of the formula within the functional form. Making TE the subject of the formula yields: ……………………………………equation 9 From equation 9 it becomes possible to find the estimates of different banks whether state-owned or joint-stock owned within Chinese banking sector as it is the main aim of this research. 2.3.2.4 Non Parametric Methods of Measurements Non-parametric methods of estimating efficiency on the other hand uses the data without specifying the functional form that applies to underlying technology in the industry, in this case the Chinese banking system or industry (Fu and Heffernan, 2007). Unlike parametric methods, non-parametric methods do not have to assume a functional form of the production process. Moreover, non-parametric methods are very flexible with respect to analyzing general technologies associated with multiple inputs and outputs (Sinani, Jones and Mygind, 2007). The following are some of the non-parametric methods with a lot of emphasis on the SFA. 2.3.2.4.1 Data Envelopment Analysis (DEA) DEA is a non-parametric method that builds a linear piece-wise function. The linear piece-wise function developed by DEA is derived from empirical observations of the various inputs and outputs within a production process. Notably, such empirical observations do not assume any priori functional correlations between the inputs and outputs. DEA then calculates efficiency based on the surface of empirically observed inputs and outputs without any assumption. Since there are no assumptions in addition to the fact that it is not possible to carry out hypothesis testing, DEA does not suffer from multicollinearity and heteroscedasticity. 2.3.2.4.2 Free Disposal Hull (FDH) FDH is a special form of the DEA model. FDH involves application of DEA vertices as well as the free disposal hull points within production that are mainly interior to DEA vertices. In this perspective, estimates derived from FDH are larger than those derived from DEA. Notably, both FDH and DEA however provide room for varying efficiency over a specified period other than the fact that they do not employ aspects of priori functional form. Like DEA, FDH is free from multicollinearity and heteroscedasticity. 2.4 Previous empirical studies that used SFA Previously, many studies have been conducted in order to identify and evaluate efficiency of various organizations through applying SFA. For instance, Xu, Guan, Jayne, and Black (2009) used SFA to identify factors influencing the profitability of fertilizer use on maize in Zambia. On the other hand, Weill, (2004) measured the cost efficiency in European banking industry. Kibaara (2005) applied SFA to test the technical efficiency in Kenya’s maize production whereas Kumar and Gulati (2008) evaluated technical efficiency and ranking of public sectors in India. Other studies included testing and evaluation of technical efficiency of the Malaysian commercial banks (Tahir and Haron, 2008) and Greek cooperative banks (Fotios, Sifodaskalakis, and Zopoundidis, 2007). The following data represent empirical studies on technical efficiency of Malaysian commercial banks through application of SFA (Tahir and Haron, 2008). The above table shows the overall performance of Malaysian banking industry on the basis of SFA. In specific terms, Malaysian commercial banks’ (domestic versus foreign) technical efficiency measured and evaluated on the basis of SFA yielded the following results (Tahir and Haron, 2008): The above figures or results were depicted in the following graph (Tahir and Haron, 2008): Through SFA, it was evident that within Malaysian banking industry, domestic banks had higher technical efficiency than foreign banks. Representing the above information on a graph, the results will be as follows: The mean technical efficiency of individual banks is illustrated in the following table (Tahir and Haron, 2008): The above table provides a summary of technical efficiency for specific commercial banks as measured and evaluated by SFA. Another set of results were obtained from the study of Greek Cooperative banks, which provided the following table that indicated efficiency of such banks through the SFA. From the data below, it is obvious that SFA is useful in measuring TE (Fotios, Sifodaskalakis, and Zopoundidis, 2007). 2.5 Critique and Application of SFA on Chinese banking system From the discussion it is evident that parametric methods or approaches for measuring efficiency are more effective and efficient than the non-parametric models (Weill, 2004). Specifically, SFA is an effective model that can easily be used in evaluating or analyzing the efficiency of commercial banks, both state-owned and joint-stock owned within China as the study intends. Some of the reasons making SFA to be an effective tool or model for this study include (Xu, 2011): i. SFA is stochastic in nature, which allows for distinguishing noise from other effects of inefficiency within the Chinese banking industry ii. SFA is parametric thereby providing vistas for hypotheses testing as well as compounding various effects of misspecification of the functional form, which encompasses both technology and inefficiency What’s more, the fact that SFA provides room for functional form estimation and assumptions make it easy to collect data and use the proposed functional form to find the level of technical efficiency within Chinese banking industry (Tong & Hing, 2003). From the collected data on inputs and outputs it will be easier and effective to apply the SFA given that hypotheses can be tested besides the fact that there are mathematical formulas applied, which enable researchers to find the best way of finding answers to technical efficiency of firms or industry (Xu and Lin, 2007). Hypothesis testing is very crucial for this research given that there are two aspects of Chinese banking sector (state-owned and joint-stock owned) that are meant to be compared (Xu et. al. 2009). For comparison purposes it is always effective to employ empirical studies and models that not only provide functional forms but also provide a room for hypothesis testing. It is also amazing that SFA takes into considerations variations, random errors. Every study must have random errors hence this make the model very efficient within Chinese banking system. 2.6 Conclusion Despite the many models or approaches of estimating technical efficiency, the merits enjoyed by SFA proves that it (SFA) is an effective method or approach to estimating the technical efficiency of various firms or sectors. What’s more, Chinese banking industry like any other banking industry in the world employs inputs to obtain outputs. Whereas there are cases where single inputs can be used to obtain multiple outputs, there are also scenarios where multiple inputs can be used to obtain single outputs. Therefore, SFA will be an effective model in evaluating technical efficiency of Chinese banking system through comparing state-owned and joint-stock owned commercial banks. Bibliography Adams, R.M., Berger, A.N. & Sickles, R.C. 1999, "Semiparametric approaches to stochastic panel frontiers with applications in the banking industry", Journal of Business & Economic Statistics, vol. 17, no. 3, pp. 349-358. China Daily. 2006. China to open renminbi market to foreign banks. November 16th, [Online; Cited April 2012]. Available from Coelli, T., Rao, D., O’donnell, C.J., and Battese, G.E., 2005, An introduction to efficiency and productivity analysis. Springer Press, New York. Du, J. and Girma, S., 2011, Cost economies, efficiency and productivity growth in the Chinese banking industry: evidence from a quarterly panel dataset, Empirical Economics, Vol. 41, No. 1, Pp. 199-226. Fu, X., and Heffernan, S. 2007. Cost X-efficiency in China’s banking sector. China Economic Review, Vol.18, No.1, pp.35-53. Greene, W., 2000, Econometric Analysis, Prentice Hall, Upper Saddle River, New Jersey. Grigorian, D. A., Manole, V., 2002, Determi­nants of Commercial Bank Performance In Transi tion. An Application of Data Envelopment Analysis. IMF Working Paper, WP/02146. Grigorian, D. and Manole, V. 2006. Determinants of commercial bank performance in transition: an application of DEA analysis. Comparative Economic Studies, Vol.48, No.3, pp.497–522. Hu, J., Su, Y. & Chen, C. 2008, "Efficiency of Nationwide Banks in China", Journal of American Academy of Business, Cambridge, vol. 13, no. 2, pp. 84-90. Huang, T., Shen, C., Chen, K. & Tseng, S. 2011, "Measuring technical and allocative efficiencies for banks in the transition countries using the Fourier flexible cost function", Journal of Productivity Analysis, vol. 35, no. 2, pp. 143-157. Inui, T., Park, J., Shin, H., Shin, D.J. & Wee, J.B. 2008, "International Comparison of Japanese and Korean Banking Efficiency/Comments and Discussion", Seoul Journal of Economics, vol. 21, no. 1, pp. 195-227. Jiang, C. (ed.), 2001, The Banking Industry in China: 2001, China Financial Publishing Housing, Beijing. Kibaara B. 2005. Technical Efficiency in Kenyan’s Maize Production: An Application of the Stochastic Frontier Approach. Thesis, Colorado State University. Kumar, S. & Gulati, R. 2008, "Evaluation of technical efficiency and ranking of public sector banks in India", International Journal of Productivity and Performance Management, vol. 57, no. 7, pp. 540-568. Kumbhakar, S. and Lovell, C., 2000, Stochastic Frontier Analysis, Cambridge University Press, Cambridge, UK. Kumbhakar, S.C. & Wang, D. 2007, "Economic reforms, efficiency and productivity in Chinese banking", Journal of Regulatory Economics, vol. 32, no. 2, pp. 105-105. Kwan, S.H. & Eisenbeis, R.A. 1996, "An analysis of inefficiencies in banking: A stochastic cost frontier approach", Economic Review - Federal Reserve Bank of San Francisco, , no. 2, pp. 16-16. Leigh, L. and Podpiera, R., 2006, The rise of foreign investment in China’s Banks-taking stock. IMF Working Paper WP/06/292. [On-line; Cited April 6, 2012], Available from - Manlagnit, M.C.V. 2009, Essays on economic efficiency of commercial banks and economic effects of foreign bank presence, University of Hawaii at Manoa. Melvin, P.D. 2008, Essays on estimating efficiency and productivity using stochastic frontier analysis, Southern Illinois University at Carbondale. Pasiouras, F., Sifodaskalakis, E., and Zopounidis, C., 2007. Estimating and analyzing the cost efficiency of Greek Cooperative banks: An application of two-stage data development analysis. [Online] [Accessed April 11, 2012]< http://www.bath.ac.uk/management/research/pdf/2007-12.pdf> People’s Daily, 2004, Foreign banks still expanding. December 5th, [On-line; Cited April 6, 2012]. Available from Sinani, E., Jones, D., and Mygind, N., 2007. Determinants of firm level technical efficiency: A Stochastic Frontier Approach. [On-line: Cited on April 6, 2012] Available from Sufian, F. 2010, "The Impact of Risk on Banks Technical and Scale Efficiency: Empirical Evidence from the Chinese Banking Sector", IUP Journal of Financial Economics, vol. 8, no. 1, pp. 82-102. Sufian, F., 2009, Analysis of the relevance of non-traditional activities items upon the efficiency of Chinese Banks: A Non-Stochastic Frontier Approach. [On-line: Cited on April 6, 2012] Available from < http://www.rebs.ro/articles/pdfs/22.pdf> Tahir, M., and Haron, S., 2008, “Technical efficiency of the Malaysian commercial banks: A stochastic frontier approach,” Banks and Bank Systems, Vol. 3, No. 4, Pp 65-72. Tong, C.S.P. & Hing, L.C. 2003, "Disparity in production efficiency of Chinas TVEs across regions: A stochastic frontier production function approach", Asia Pacific Journal of Management, vol. 20, no. 1, pp. 113-113. Tortosa-Ausina, E., Grifell-Tatje, E. & C. Armero, D. C., 2008, “Sensitivity analysis of efficiency and Malmquist productivity indices: An application to Spanish savings banks”, European Journal of Operational Research Vol. 184, Pp. 1062-1084. Weill, L. 2004, "Measuring Cost Efficiency in European Banking: A Comparison of Frontier Techniques", Journal of Productivity Analysis, vol. 21, no. 2, pp. 133-152. Xu, L., and Lin, C., 2007, Can Chinese banks compete after accession to WTO? Journal of Asian Economics, Vol.18, No.6, pp.883-903. Xu, Z., 2011, Technical, Pure Technical and Scale Efficiency of China's Banking Industry, International Conference on Information Management, Innovation Management, and Industrial Engineering, November 26th-27th. Xu, Z., Guan, Z., Jayne, T., and Black, 2009, “Factors influencing the profitability of fertilizer use on maize in Zambia, Agricultural Economics, Vol. 40. Read More